Publication: Evaluation of cone-beam computed tomography diagnostic image quality using cluster signal-to-noise analysis
Issued Date
2019-01-22
Resource Type
ISSN
16139674
09116028
09116028
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2-s2.0-85044084256
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Mahidol University
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SCOPUS
Bibliographic Citation
Oral Radiology. Vol.35, No.1 (2019), 59-67
Suggested Citation
Warangkana Weerawanich, Mayumi Shimizu, Yohei Takeshita, Kazutoshi Okamura, Shoko Yoshida, Gainer R. Jasa, Kazunori Yoshiura Evaluation of cone-beam computed tomography diagnostic image quality using cluster signal-to-noise analysis. Oral Radiology. Vol.35, No.1 (2019), 59-67. doi:10.1007/s11282-018-0325-0 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/50726
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Title
Evaluation of cone-beam computed tomography diagnostic image quality using cluster signal-to-noise analysis
Abstract
© 2018, Japanese Society for Oral and Maxillofacial Radiology and Springer Nature Singapore Pte Ltd. Objectives: (1) We sought to assess correlation among four representative parameters from a cluster signal-to-noise curve (true-positive rate [TPR] corresponding to background noise, accuracy corresponding to background noise, maximum TPR, and maximum accuracy) and the diagnostic accuracy of the identification of the mandibular canal using data from observers in a previous study, under the same exposure conditions. (2) We sought to clarify the relationship between the hole depths of a phantom and diagnostic accuracy. Methods: CBCT images of a Teflon plate phantom with holes of decreasing depths from 0.7 to 0.1 mm were analyzed using the FindFoci plugin of ImageJ. Subsequently, we constructed cluster signal-to-noise curves by plotting TPRs against false-positive rates. The four parameters were assessed by comparing with the diagnostic accuracy calculated from the observers. To analyze image contrast ranges related to detection of mandibular canals, we determined five ranges of hole depths, to represent different contrast ranges—0.1–0.7, 0.1–0.5, 0.2–0.6, 0.2–0.7 and 0.3–0.7 mm—and compared them with observers’ diagnostic accuracy. Results: Among the four representative parameters, accuracy corresponding to background noise had the highest correlation with the observers’ diagnostic accuracy. Hole depths of 0.3–0.7 and 0.1–0.7 mm had the highest correlation with observers’ diagnostic accuracy in mandibles with distinct and indistinct mandibular canals, respectively. Conclusions: The accuracy corresponding to background noise obtained from the cluster signal-to-noise curve can be used to evaluate the effects of exposure conditions on diagnostic accuracy.